Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression

Abstract

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent work on the "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications because it combines state-of-theart techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations, and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression as well as to suggest novel experimentally verifiable hypotheses.cancer evolution | selective advantage | Bayesian structural inference | next generation sequencing | causality

Beyond Computational Genomics towards Systems Metabolomics for Precision Oncology

“…5C), in which DNA damage response (DDR), mismatch repair, aneuploidy and similar processes play a role, increasing the mutation load of developing tumors (69). Given that trajectories of mutations appear to follow detectable patterns (70,71,72) and that nutrient-sensing and metabolic pathways have been reported to interfere with DDR (73), also this biological process may open new ways to drug discovery.…”

How “precise” is precision medicine in hematology?

“…sequence colonies arising from single cells. Statistical methods to infer the order of acquisition of multiple mutations in a cancer were recently suggested by Papaemmanuil et al 19 and Caravagna et al 20 These methods are important as they show that cancer progression follows a defined trajectory, not a random pattern. However they are valid for groups of patients, but cannot assess the order of development of mutations inside a single leukemia.…”

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data

“…We further distinguish [2]: (i) ensemble-level progression models, describing the statistical trends of accumulation of genomic alterations in a cohort of distinct cancer patients, and (ii) individuallevel models, thus accounting for the specific evolutionary history of cancer clones in individual tumors.…”

“…TRONCO, in its current form and in perspective, should be thought of as a tool that provides the implementation of up-to-date solutions to the progression inference problem. At the time of this writing, for instance, it is the ideal conclusive stage of a modular pipeline for the extraction of ensemble-level cancer progression models from cross-sectional data [2]. In such a pipeline input data are pre-processed to (i) stratify samples in tumor subtypes, (ii) select driver alterations and (iii) identify groups of fitness-equivalent (i.e., mutually exclusive) alterations, prior to the application of the CAPRI algorithm.…”